\section{Conclusion and Future Work}
\label{sec:conclude}
In this paper, we have analytically investigate the object tracking in MCS.
Two stage approach,
namely a cluster representation based N-Gram model
and the MUTA,
have been exploited to
predict the object movement offline
and make optimal task assignment decision online, successively.
System measures of the approach,
e.g., the movement prediction accuracy,
the number of sensing location, cost, benefit and utility have been explored.
The result confirms that our proposed MobiTrack
can help the system achieve maximum utility
on object tracking compared
with other classical benchmarks.
Our future work will focus on
the location learning process.
We will further investigate
whether the settings of the time threshold
and the radius for clustering
would influence the movement prediction accuracy,
and determine a more reasonable time threshold and
radius and possibly employ deep learning to
solve this problem.






